Prashanth.S 19MID0020

Bivariate Analysis

Importing the libraries

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
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##     combine

library(tidyr)
library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
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##     smiths

library(RColorBrewer)
library(ggrepel)
library(ggthemes)
library(GGally)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
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##     last_plot

## The following object is masked from 'package:stats':
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##     filter

## The following object is masked from 'package:graphics':
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##     layout

Importing the data-sets

df = read.csv('pokemon.csv')
head(df)

PreProcessing

cat("Number of instances  : ",nrow(df))

## Number of instances  :  800

cat("\nNumber of attributes : ",ncol(df))

## 
## Number of attributes :  12

str(df)

## 'data.frame':    800 obs. of  12 variables:
##  $ Name      : chr  "Bulbasaur" "Ivysaur" "Venusaur" "VenusaurMega Venusaur" ...
##  $ Type.1    : chr  "Grass" "Grass" "Grass" "Grass" ...
##  $ Type.2    : chr  "Poison" "Poison" "Poison" "Poison" ...
##  $ Total     : int  318 405 525 625 309 405 534 634 634 314 ...
##  $ HP        : int  45 60 80 80 39 58 78 78 78 44 ...
##  $ Attack    : int  49 62 82 100 52 64 84 130 104 48 ...
##  $ Defense   : int  49 63 83 123 43 58 78 111 78 65 ...
##  $ Sp..Atk   : int  65 80 100 122 60 80 109 130 159 50 ...
##  $ Sp..Def   : int  65 80 100 120 50 65 85 85 115 64 ...
##  $ Speed     : int  45 60 80 80 65 80 100 100 100 43 ...
##  $ Generation: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Legendary : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...

summary(df)

##      Name              Type.1             Type.2              Total      
##  Length:800         Length:800         Length:800         Min.   :180.0  
##  Class :character   Class :character   Class :character   1st Qu.:330.0  
##  Mode  :character   Mode  :character   Mode  :character   Median :450.0  
##                                                           Mean   :435.1  
##                                                           3rd Qu.:515.0  
##                                                           Max.   :780.0  
##        HP             Attack       Defense          Sp..Atk      
##  Min.   :  1.00   Min.   :  5   Min.   :  5.00   Min.   : 10.00  
##  1st Qu.: 50.00   1st Qu.: 55   1st Qu.: 50.00   1st Qu.: 49.75  
##  Median : 65.00   Median : 75   Median : 70.00   Median : 65.00  
##  Mean   : 69.26   Mean   : 79   Mean   : 73.84   Mean   : 72.82  
##  3rd Qu.: 80.00   3rd Qu.:100   3rd Qu.: 90.00   3rd Qu.: 95.00  
##  Max.   :255.00   Max.   :190   Max.   :230.00   Max.   :194.00  
##     Sp..Def          Speed          Generation    Legendary      
##  Min.   : 20.0   Min.   :  5.00   Min.   :1.000   Mode :logical  
##  1st Qu.: 50.0   1st Qu.: 45.00   1st Qu.:2.000   FALSE:735      
##  Median : 70.0   Median : 65.00   Median :3.000   TRUE :65       
##  Mean   : 71.9   Mean   : 68.28   Mean   :3.324                  
##  3rd Qu.: 90.0   3rd Qu.: 90.00   3rd Qu.:5.000                  
##  Max.   :230.0   Max.   :180.00   Max.   :6.000

for ( col in 1:ncol(df)) { 
  cat(colnames(df)[col])
  cat(" --> ")
  print(sum(is.na(df[,col])))
}

## Name --> [1] 0
## Type.1 --> [1] 0
## Type.2 --> [1] 0
## Total --> [1] 0
## HP --> [1] 0
## Attack --> [1] 0
## Defense --> [1] 0
## Sp..Atk --> [1] 0
## Sp..Def --> [1] 0
## Speed --> [1] 0
## Generation --> [1] 0
## Legendary --> [1] 0

From the above observation, it seems that there are no missing values. But there are more empty values(i.e missing values) in the data-set

Converting the empty values in missing values

df[df == ""] <- NA

for ( col in 1:ncol(df)) { 
  cat(colnames(df)[col])
  cat(" --> ")
  print(sum(is.na(df[,col])))
}

## Name --> [1] 0
## Type.1 --> [1] 0
## Type.2 --> [1] 386
## Total --> [1] 0
## HP --> [1] 0
## Attack --> [1] 0
## Defense --> [1] 0
## Sp..Atk --> [1] 0
## Sp..Def --> [1] 0
## Speed --> [1] 0
## Generation --> [1] 0
## Legendary --> [1] 0

print("Percentage of Missing values in Type.2 attribute")

## [1] "Percentage of Missing values in Type.2 attribute"

print((abs(nrow(df) - sum(is.na(df['Type.2'])))) / nrow(df))

## [1] 0.5175

There are 51% missing values in ‘Type-2’ attribute

1)Bar Chart

ggplot(df, 
       aes(x = Type.1, fill = Legendary)) + 
       geom_bar(position = "stack", color='black') + 
       labs(x = "Type-1", y = "Count", title  = "Contribution of Type-1 and Legendary") + 
       coord_flip() 

Number of Pokemon by Type-1

df %>%
  group_by(Type.1) %>% summarise(number = n()) %>%
  ggplot(aes(x = reorder(Type.1, number), y = number , fill = Type.1)) +
  geom_bar(stat = 'identity', color='black') +
  labs(x = "Type-1 of Pokemon", y = "Number of Pokemon", title  = "Number of Pokemon by Type-1") + 
  coord_flip() + geom_text(aes(label = number), hjust = -1.0)

Number of Pokemon by Type-2

df %>%filter(Type.2 != '') %>% group_by(Type.2) %>% summarise(number = n()) %>%
  ggplot(aes(x = reorder(Type.2, number), y = number , fill = Type.2)) +
  geom_bar(stat = 'identity', color='black') +
  labs(x = "Type-2 of Pokemon", y = "Number of Pokemon", title  = "Number of Pokemon by Type-2") + 
  coord_flip() + geom_text(aes(label = number), hjust = -1.0)

2)Scatter plot

Pokemons with higher attack ratings are faster.

ggplot(df, aes(Attack, Defense)) + geom_jitter(aes(col=Speed)) + 
        scale_color_gradient(low="blue", high="darkorange") + 
        ggtitle("Defense vs Attack wrt Speed")

Correlation Matrix

ggpairs(df, columns = c('Attack', 'Defense', 'HP', 'Sp..Atk', 'Sp..Def', 'Speed')) +
         theme_bw() +labs(title = 'Correlation Matrix of Pokemon Stats')

3)Density Plot(Univariate analysis)

density_hp =  ggplot(data=df, aes(HP)) + 
              geom_density(col="white",fill="pink", alpha=0.8) + 
              ggtitle("Density Plot of HP")

density_speed = ggplot(data=df, aes(Speed)) + 
                geom_density(col="white", fill="darkorchid", alpha=0.8) + 
                ggtitle("Density Plot of Speed Characterstics")

density_attack = ggplot(data=df, aes(Attack)) + 
                 geom_density(col="white", fill="orange", alpha=0.7) + 
                 ggtitle("Density Plot of Attack Characterstics")

density_defense = ggplot(data=df, aes(Defense)) + 
                  geom_density(col="white", fill="firebrick", alpha=0.7) + 
                  ggtitle("Density Plot of Defense Characterstics")

grid.arrange(density_hp, density_speed, density_attack, density_defense, ncol=2)

4)Box-Plot(Multi-variate Analysis)

Score of Pokemon by generation

## HP(Highest Power) --> Key
## Speed --> Value

df %>% gather(key, value, HP:Speed) %>% ggplot(aes(x = Generation, y = value, fill = as.factor(Generation))) +
       geom_boxplot() + facet_grid(~key) + labs(x="Generation", y="Score", title="Various score based on Generation flag") 

Score of Pokemon by Legendary type

## HP(Highest Power) --> Key
## Speed --> Value
df %>% gather(key, value, HP:Speed) %>% ggplot(aes(x=Legendary, y=value, fill=as.factor(Legendary))) +
       geom_boxplot() + facet_grid(~key) + labs(x="Lengendry", y="Score", title="Various score based on Lengendry flag")

5) Pie-Chart(Multi-Variate Analysis)

df1 = data.frame(unique(df$Type.1),aggregate(df,by=list(df$Type.1),FUN=median)["HP"])

## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]): argument
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plot_ly(df1,labels=~unique.df.Type.1.,values =~HP,type ="pie")